Data Science Project
Budget / Salary$200–300
TypeFreelance project
LocationRemote
Posted51 minutes ago
## IMPORTANT – Communication, Approval & Documentation Requirements
**Please read these requirements carefully before accepting the project. They are mandatory.**
1. **Prior Approval Before Every Major Step:** You must consult me and obtain my approval before making any important project-related decision or taking any major action. This includes, but is not limited to:
- Selecting the geographic location and time period.
- Deciding on the dataset scope, sample size, and filtering criteria.
- Choosing data-cleaning and preprocessing methods.
- Deciding how to label and classify customer reviews.
- Selecting annotation rules and NLP techniques.
- Choosing statistical methods, models, and evaluation metrics.
- Excluding variables, records, or data sources.
- Making any significant changes to the research scope or methodology.
**Do not make assumptions, change the project scope, or proceed with major decisions without discussing them with me and receiving my approval first.**
2. **Clear and Comprehensive Documentation:** You must maintain detailed documentation of everything you do throughout the project, not just provide the final results. This includes:
- Every step performed during data acquisition, filtering, integration, cleaning, and transformation.
- The reason behind every important technical or analytical decision.
- All annotation guidelines, labeling rules, and examples.
- Data-quality issues discovered and how they were handled.
- Statistical methods, formulas, assumptions, and interpretations.
- Code explanations and instructions for reproducing the results.
- Any changes made to the original datasets and the reasons for those changes.
3. **Regular Progress Updates:** Work must be divided into clear milestones. At the end of each milestone, provide the completed files, documentation, and a summary of what was done. Wait for my review and approval before moving to the next major stage.
4. **Knowledge Transfer:** All technical work must be explained clearly enough for someone with a data science background to understand, reproduce, and defend the methodology and results.
**The objective is not simply to deliver a finished analysis. The objective is to ensure that every step is transparent, documented, reproducible, and approved before proceeding.**
**Please read these requirements carefully before accepting the project. They are mandatory.**
1. **Prior Approval Before Every Major Step:** You must consult me and obtain my approval before making any important project-related decision or taking any major action. This includes, but is not limited to:
- Selecting the geographic location and time period.
- Deciding on the dataset scope, sample size, and filtering criteria.
- Choosing data-cleaning and preprocessing methods.
- Deciding how to label and classify customer reviews.
- Selecting annotation rules and NLP techniques.
- Choosing statistical methods, models, and evaluation metrics.
- Excluding variables, records, or data sources.
- Making any significant changes to the research scope or methodology.
**Do not make assumptions, change the project scope, or proceed with major decisions without discussing them with me and receiving my approval first.**
2. **Clear and Comprehensive Documentation:** You must maintain detailed documentation of everything you do throughout the project, not just provide the final results. This includes:
- Every step performed during data acquisition, filtering, integration, cleaning, and transformation.
- The reason behind every important technical or analytical decision.
- All annotation guidelines, labeling rules, and examples.
- Data-quality issues discovered and how they were handled.
- Statistical methods, formulas, assumptions, and interpretations.
- Code explanations and instructions for reproducing the results.
- Any changes made to the original datasets and the reasons for those changes.
3. **Regular Progress Updates:** Work must be divided into clear milestones. At the end of each milestone, provide the completed files, documentation, and a summary of what was done. Wait for my review and approval before moving to the next major stage.
4. **Knowledge Transfer:** All technical work must be explained clearly enough for someone with a data science background to understand, reproduce, and defend the methodology and results.
**The objective is not simply to deliver a finished analysis. The objective is to ensure that every step is transparent, documented, reproducible, and approved before proceeding.**
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